Crack Severity Classification from Timber Cross-Sectional Images Using Convolutional Neural Network

Kato, Shigeru; Wada, Naoki; Shiogai, Kazuki; Tamaki, Takashi; Kagawa, Tomomichi; Toyosaki, Renon; Nobuhara, Hajime

doi:10.3390/app13031280

Cited by 5 publications

(1 citation statement)

References 19 publications

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“…The CNN and conditional generation antagonism model were utilized by Liu et al to predict the cross-sectional shape and damage morphology of self-piercing riveted joints in carbon fiber-reinforced composites and aluminum alloy [ 31 ]. Recently, Kato et al evaluated the internal cracks of timbers using CNNs [ 32 , 33 ]. The optimal thickness of blending composite laminates was determined by Huynh et al using the CNN and genetic algorithm [ 34 ].…”

Section: Introductionmentioning

confidence: 99%

In-Situ Classification of Highly Deformed Corrugated Board Using Convolution Neural Networks

Rogalka,

Grabski,

Garbowski

2024

Sensors

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The extensive use of corrugated board in the packaging industry is attributed to its excellent cushioning, mechanical properties, and environmental benefits like recyclability and biodegradability. The integrity of corrugated board depends on various factors, including its geometric design, paper quality, the number of layers, and environmental conditions such as humidity and temperature. This study introduces an innovative application of convolutional neural networks (CNNs) for analyzing and classifying images of corrugated boards, particularly those with deformations. For this purpose, a special device with advanced imaging capabilities, including a high-resolution camera and image sensor, was developed and used to acquire detailed cross-section images of the corrugated boards. The samples of seven types of corrugated board were studied. The proposed approach involves optimizing CNNs to enhance their classification performance. Despite challenges posed by deformed samples, the methodology demonstrates high accuracy in most cases, though a few samples posed recognition difficulties. The findings of this research are significant for the packaging industry, offering a sophisticated method for quality control and defect detection in corrugated board production. The best classification accuracy obtained achieved more than 99%. This could lead to improved product quality and reduced waste. Additionally, this study paves the way for future research on applying machine learning for material quality assessment, which could have broader implications beyond the packaging sector.

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Section: Introductionmentioning

confidence: 99%

In-Situ Classification of Highly Deformed Corrugated Board Using Convolution Neural Networks

Rogalka,

Grabski,

Garbowski

2024

Sensors

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Evaluation of the Timber Internal Crack Using CNN

Toyosaki,

Kato,

Tamaki

et al. 2023

Lecture Notes on Data Engineering and Communications Technologies

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Effects of Wood Drying Temperatures on the Reduction in Mechanical Properties of Japanese Cedar (Cryptomeria japonica D. Don) Perpendicular to Grain

Toba,

Nakai,

Saito

2024

Buildings

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Wood drying is often accompanied by changes in mechanical properties due to external thermal energy. This study examined the influences of drying temperature on the mechanical properties of Japanese cedar based on the partial compression properties and bending properties. Two types of longitudinal specimens with quarter grain on both lateral surfaces were prepared under green conditions, followed by subsequent drying under each drying temperature (20 °C, 40 °C, 60 °C, 80 °C, and 100 °C). Then, the partial compression and bending tests were performed using the respective specimens. Young’s modulus perpendicular to grain, yield stress, and compressive strength obtained by the partial compressive test were highest for wood dried at 20 °C. It was considered that the decrease in mechanical properties was attributed to the thermal influences during drying at 100 °C and other factors such as compressive residual stress and cell walls collapsing at lower temperatures. The strain energy suggested that the effects of drying temperature became apparent, especially in the plastic region during loading in the direction perpendicular to the wood fiber. Bending properties showed little influence on drying temperatures compared to the partial compressive properties, whereas the fractures found under the loading point of the wood dried at 100 °C suggested a reduction in resistance to shear forces.

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Crack Severity Classification from Timber Cross-Sectional Images Using Convolutional Neural Network

Cited by 5 publications

References 19 publications

In-Situ Classification of Highly Deformed Corrugated Board Using Convolution Neural Networks

In-Situ Classification of Highly Deformed Corrugated Board Using Convolution Neural Networks

Evaluation of the Timber Internal Crack Using CNN

Effects of Wood Drying Temperatures on the Reduction in Mechanical Properties of Japanese Cedar (Cryptomeria japonica D. Don) Perpendicular to Grain

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